Improving the Electrokinetic Properties of PDMS With Surface Treatments
Bibliographic record
Abstract
PDMS (Polydimethylsiloxane) is widely used as a microfluidic chip material for various applications due to its desirable properties [1, 2]. However PDMS has several drawbacks that limit its utilization in a number of microfluidic applications [1–4]. Properties such as the hydrophobic nature, sample absorption, and low electrokinetic properties (low zeta potential) are some issues that must be considered before using PDMS for numerous applications [3]. In many PDMS based chips electroosmotic pumping is used for fluid flow and sample transport along the microchannel networks. Simplicity of implementation in microfluidic chips, fast response time, and the plug-like velocity profile are the major advantages of electroosmotic flow compared to other fluid pumping techniques [2]. This type of flow utilizes the formation of electric double layer (EDL) in microchannels and the movement of ions under an applied external electric field. Thus, the surface properties of the channel material and liquid properties (ionic concentration, pH, and viscosity) play major roles in electroosmotic pumping for different solutions in microchannels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".